{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Man</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He\\s Not Really into Dudes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beautiful Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin Longblade</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robo Slayer 3000</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amped II</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         电影名称  打斗镜头  接吻镜头 label\n",
       "0              California Man     3   104   爱情片\n",
       "1  He\\s Not Really into Dudes     2   100   爱情片\n",
       "2             Beautiful Woman     1    81   爱情片\n",
       "3             Kevin Longblade   101    10   动作片\n",
       "4            Robo Slayer 3000    99     5   动作片\n",
       "5                    Amped II    98     2   动作片"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "\"电影名称\":[\"California Man\",\"He\\s Not Really into Dudes\",\"Beautiful Woman\",\"Kevin Longblade\",\"Robo Slayer 3000\",\"Amped II\"],\n",
    "\"打斗镜头\":[3,2,1,101,99,98],\n",
    "\"接吻镜头\":[104,100,81,10,5,2],\n",
    "\"label\":[\"爱情片\",\"爱情片\",\"爱情片\",\"动作片\",\"动作片\",\"动作片\"],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn(inX, df, k):\n",
    "    df = df.copy()\n",
    "    df1 = (df.iloc[:,1:-1] - inX) ** 2\n",
    "\n",
    "    df['dist'] = df1.sun(axis=1)\n",
    "    df.sort_values(axis=0, by='dist', ascending=True)\n",
    "\n",
    "    return df.iloc[:k,:].value_counts('label').index[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>打斗镜头</th>\n",
       "      <th>接吻镜头</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Man</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He\\s Not Really into Dudes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beautiful Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>爱情片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin Longblade</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robo Slayer 3000</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amped II</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>动作片</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         电影名称  打斗镜头  接吻镜头 label\n",
       "0              California Man     3   104   爱情片\n",
       "1  He\\s Not Really into Dudes     2   100   爱情片\n",
       "2             Beautiful Woman     1    81   爱情片\n",
       "3             Kevin Longblade   101    10   动作片\n",
       "4            Robo Slayer 3000    99     5   动作片\n",
       "5                    Amped II    98     2   动作片"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[len(df)] = {\n",
    "    '电影名称' :\"?\",\n",
    "    '打斗镜头' : 18,\n",
    "    \"接吻镜头\" : 90,\n",
    "    'lable' : \"?\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'kiss')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df.iloc[:,1], y=df.iloc[:,2],c=['green','green','green','blue','blue', 'blue', 'black'])\n",
    "plt.xlabel('fight')\n",
    "plt.ylabel('kiss')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit ('3.10.6')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6 (main, Aug 22 2022, 11:52:08) [GCC 9.4.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d44d76ef8cbbc4331cecfe2e59228ac31ebb71026289858a116838be7168b60b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
